date_ini = '2019/09/01/00'
date_end = '2019/09/10/00'
gliders = retrieve_dataset_id_erddap_server(url_erddap, lat_lim, lon_lim,
date_ini, date_end)
dataset_id = gliders[10]
# variable to retrieve
var_name = 'temperature'
#var_name = 'salinity'
kwargs = dict(date_ini=date_ini, date_end=date_end)
scatter_plot = 'yes'
tempg, saltg, timeg, latg, long, depthg = read_glider_data_erddap_server(url_erddap,dataset_id,\
lat_lim,lon_lim,scatter_plot,**kwargs)
#tempg, saltg, timeg, latg, long, depthg = read_glider_data_erddap_server(url_erddap,dataset_id,\
# lat_lim,lon_lim,scatter_plot)
contour_plot = 'yes' # default value is 'yes'
delta_z = 0.4 # default value is 0.3
tempg_gridded, timegg, depthg_gridded = \
grid_glider_data(var_name,dataset_id,tempg,timeg,latg,long,depthg,delta_z,contour_plot)
#%% cell #6: Search for glider data sets given a
# latitude and longitude box and time window, choose one those data sets
# (dataset_id), grid in the vertical the glider transect, get the glider
# transect in the GOFS 3.1 grid, and plot both the transect from the glider
# deployment and GOFS 3.1 output
ttm = model.time
tm = netCDF4.num2date(ttm[:],ttm.units)
tmin = datetime.datetime.strptime(date_ini,'%Y-%m-%dT%H:%M:%SZ')
tmax = datetime.datetime.strptime(date_end,'%Y-%m-%dT%H:%M:%SZ')
oktimem = np.where(np.logical_and(tm >= tmin, tm <= tmax))
timem = tm[oktimem]
#%%
# Read and process glider data
print('Reading glider data')
df = read_glider_data_erddap_server(url_glider,dataset_id,var_glider,\
lat_lim,lon_lim,date_ini,date_end,\
scatter_plot='no')
if len(df) != 0:
depthg_gridded, varg_gridded, timeg, latg, long = \
grid_glider_data_erddap(df,dataset_id,var_glider,delta_z=0.2,contour_plot='no')
# Conversion from glider longitude and latitude to GOFS convention
target_lon = np.empty((len(long),))
target_lon[:] = np.nan
for i,ii in enumerate(long):
if ii < 0:
target_lon[i] = 360 + ii
else:
target_lon[i] = ii
